Extracting essential data from a dataset and displaying it is a necessary part of data science; therefore individuals can make correct decisions based on the data. In this assignment, you will extract some stock data, you will then display this data in a graph.
Estimated Time Needed: 30 min
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import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In this section, we define the function make_graph. You don't have to know how the function works, you should only care about the inputs. It takes a dataframe with stock data (dataframe must contain Date and Close columns), a dataframe with revenue data (dataframe must contain Date and Revenue columns), and the name of the stock.
def make_graph(stock_data, revenue_data, stock):
fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
fig.update_xaxes(title_text="Date", row=1, col=1)
fig.update_xaxes(title_text="Date", row=2, col=1)
fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
fig.update_layout(showlegend=False,
height=900,
title=stock,
xaxis_rangeslider_visible=True)
fig.show()
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is Tesla and its ticker symbol is TSLA.
tsla = yf.Ticker('TSLA')
Using the ticker object and the function history extract stock information and save it in a dataframe named tesla_data. Set the period parameter to max so we get information for the maximum amount of time.
tesla_data = tsla.history(period = 'max')
Reset the index using the reset_index(inplace=True) function on the tesla_data DataFrame and display the first five rows of the tesla_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 1 to the results below.
tesla_data.reset_index(inplace=True)
tesla_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2010-06-29 | 1.266667 | 1.666667 | 1.169333 | 1.592667 | 281494500 | 0 | 0.0 |
| 1 | 2010-06-30 | 1.719333 | 2.028000 | 1.553333 | 1.588667 | 257806500 | 0 | 0.0 |
| 2 | 2010-07-01 | 1.666667 | 1.728000 | 1.351333 | 1.464000 | 123282000 | 0 | 0.0 |
| 3 | 2010-07-02 | 1.533333 | 1.540000 | 1.247333 | 1.280000 | 77097000 | 0 | 0.0 |
| 4 | 2010-07-06 | 1.333333 | 1.333333 | 1.055333 | 1.074000 | 103003500 | 0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm Save the text of the response as a variable named html_data.
url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/revenue.htm'
html_data = requests.get(url).text
Parse the html data using beautiful_soup.
beautiful_soup = BeautifulSoup(html_data, 'html.parser')
#Using `BeautifulSoup` or the `read_html` function extract the table with `Tesla Quarterly Revenue` and store it into a dataframe named `tesla_revenue`. The dataframe should have columns `Date` and `Revenue`.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
tables = beautiful_soup.find_all('table')
len(tables)
for index, table in enumerate(tables):
if ('Tesla Quarterly Revenue' in str(table)):
table_index = index
print(table_index)
#print(tables[1].prettify())
tesla_revenue = pd.DataFrame(columns=['Date', 'Revenue'])
for row in tables[1].tbody.find_all('tr'):
col = row.find_all('td')
if (col != []):
date = col[0].text.strip()
revenue = col[1].text.strip()
tesla_revenue = tesla_revenue.append({'Date':date, 'Revenue':revenue},ignore_index = True)
print(tesla_revenue)
1
Date Revenue
0 2022-09-30 $21,454
1 2022-06-30 $16,934
2 2022-03-31 $18,756
3 2021-12-31 $17,719
4 2021-09-30 $13,757
5 2021-06-30 $11,958
6 2021-03-31 $10,389
7 2020-12-31 $10,744
8 2020-09-30 $8,771
9 2020-06-30 $6,036
10 2020-03-31 $5,985
11 2019-12-31 $7,384
12 2019-09-30 $6,303
13 2019-06-30 $6,350
14 2019-03-31 $4,541
15 2018-12-31 $7,226
16 2018-09-30 $6,824
17 2018-06-30 $4,002
18 2018-03-31 $3,409
19 2017-12-31 $3,288
20 2017-09-30 $2,985
21 2017-06-30 $2,790
22 2017-03-31 $2,696
23 2016-12-31 $2,285
24 2016-09-30 $2,298
25 2016-06-30 $1,270
26 2016-03-31 $1,147
27 2015-12-31 $1,214
28 2015-09-30 $937
29 2015-06-30 $955
30 2015-03-31 $940
31 2014-12-31 $957
32 2014-09-30 $852
33 2014-06-30 $769
34 2014-03-31 $621
35 2013-12-31 $615
36 2013-09-30 $431
37 2013-06-30 $405
38 2013-03-31 $562
39 2012-12-31 $306
40 2012-09-30 $50
41 2012-06-30 $27
42 2012-03-31 $30
43 2011-12-31 $39
44 2011-09-30 $58
45 2011-06-30 $58
46 2011-03-31 $49
47 2010-12-31 $36
48 2010-09-30 $31
49 2010-06-30 $28
50 2010-03-31 $21
51 2009-12-31
52 2009-09-30 $46
53 2009-06-30 $27
Execute the following line to remove the comma and dollar sign from the Revenue column.
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|$',"")
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. """Entry point for launching an IPython kernel.
Execute the following lines to remove an null or empty strings in the Revenue column.
tesla_revenue.dropna(inplace=True)
tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]
Display the last 5 row of the tesla_revenue dataframe using the tail function. Take a screenshot of the results.
tesla_revenue.tail()
| Date | Revenue | |
|---|---|---|
| 48 | 2010-09-30 | 31 |
| 49 | 2010-06-30 | 28 |
| 50 | 2010-03-31 | 21 |
| 52 | 2009-09-30 | 46 |
| 53 | 2009-06-30 | 27 |
Using the Ticker function enter the ticker symbol of the stock we want to extract data on to create a ticker object. The stock is GameStop and its ticker symbol is GME.
gme = yf.Ticker('GME')
Using the ticker object and the function history extract stock information and save it in a dataframe named gme_data. Set the period parameter to max so we get information for the maximum amount of time.
gme_data = gme.history(period = 'max')
Reset the index using the reset_index(inplace=True) function on the gme_data DataFrame and display the first five rows of the gme_data dataframe using the head function. Take a screenshot of the results and code from the beginning of Question 3 to the results below.
gme_data.reset_index(inplace=True)
gme_data.head()
| Date | Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2002-02-13 | 1.620128 | 1.693349 | 1.603295 | 1.691666 | 76216000 | 0.0 | 0.0 |
| 1 | 2002-02-14 | 1.712707 | 1.716073 | 1.670626 | 1.683250 | 11021600 | 0.0 | 0.0 |
| 2 | 2002-02-15 | 1.683251 | 1.687459 | 1.658002 | 1.674834 | 8389600 | 0.0 | 0.0 |
| 3 | 2002-02-19 | 1.666418 | 1.666418 | 1.578047 | 1.607504 | 7410400 | 0.0 | 0.0 |
| 4 | 2002-02-20 | 1.615920 | 1.662209 | 1.603296 | 1.662209 | 6892800 | 0.0 | 0.0 |
Use the requests library to download the webpage https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html. Save the text of the response as a variable named html_data.
url_4 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'
html_data = requests.get(url_4).text
Parse the html data using beautiful_soup.
soup_4 = BeautifulSoup(html_data, 'html.parser')
Using BeautifulSoup or the read_html function extract the table with GameStop Quarterly Revenue and store it into a dataframe named gme_revenue. The dataframe should have columns Date and Revenue. Make sure the comma and dollar sign is removed from the Revenue column using a method similar to what you did in Question 2.
Below is the code to isolate the table, you will now need to loop through the rows and columns like in the previous lab
soup.find_all("tbody")[1]
If you want to use the read_html function the table is located at index 1
tables = soup_4.find_all('table')
len(tables)
for index, table in enumerate(tables):
if ('GameStop Quarterly Revenue' in str(table)):
table_index = index
print(table_index)
#print(tables[1].prettify())
gme_revenue = pd.DataFrame(columns=['Date', 'Revenue'])
for row in tables[1].tbody.find_all('tr'):
col = row.find_all('td')
if (col != []):
date = col[0].text.strip()
revenue = col[1].text.strip()
gme_revenue = gme_revenue.append({'Date':date, 'Revenue':revenue},ignore_index = True)
print(gme_revenue)
1
Date Revenue
0 2020-04-30 $1,021
1 2020-01-31 $2,194
2 2019-10-31 $1,439
3 2019-07-31 $1,286
4 2019-04-30 $1,548
.. ... ...
57 2006-01-31 $1,667
58 2005-10-31 $534
59 2005-07-31 $416
60 2005-04-30 $475
61 2005-01-31 $709
[62 rows x 2 columns]
gme_revenue["Revenue"] = gme_revenue['Revenue'].str.replace(',|\$',"")
/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: The default value of regex will change from True to False in a future version. """Entry point for launching an IPython kernel.
gme_revenue.dropna(inplace=True)
gme_revenue = gme_revenue[gme_revenue['Revenue'] != ""]
Display the last five rows of the gme_revenue dataframe using the tail function. Take a screenshot of the results.
gme_revenue.tail()
| Date | Revenue | |
|---|---|---|
| 57 | 2006-01-31 | 1667 |
| 58 | 2005-10-31 | 534 |
| 59 | 2005-07-31 | 416 |
| 60 | 2005-04-30 | 475 |
| 61 | 2005-01-31 | 709 |
Use the make_graph function to graph the Tesla Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(tesla_data, tesla_revenue, 'Tesla'). Note the graph will only show data upto June 2021.
make_graph(tesla_data, tesla_revenue, 'Tesla')
Use the make_graph function to graph the GameStop Stock Data, also provide a title for the graph. The structure to call the make_graph function is make_graph(gme_data, gme_revenue, 'GameStop'). Note the graph will only show data upto June 2021.
make_graph(gme_data, gme_revenue, 'GameStop')
Joseph Santarcangelo has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.
Azim Hirjani
| Date (YYYY-MM-DD) | Version | Changed By | Change Description |
|---|---|---|---|
| 2022-02-28 | 1.2 | Lakshmi Holla | Changed the URL of GameStop |
| 2020-11-10 | 1.1 | Malika Singla | Deleted the Optional part |
| 2020-08-27 | 1.0 | Malika Singla | Added lab to GitLab |